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Traditionally, mutation testing generates an abundance of small deviations ofa program, called mutants. At industrial systems the scale and size ofFacebook's, doing this is infeasible. We should not create mutants that thetest suite would likely fail on or that give no actionable signal todevelopers. To tackle this problem, in this paper, we semi-automatically learnerror-inducing patterns from a corpus of common Java coding errors and fromchanges that caused operational anomalies at Facebook specifically. We combinethe mutations with instrumentation that measures which tests exactly visitedthe mutated piece of code. Results on more than 15,000 generated mutants showthat more than half of the generated mutants survive Facebook's rigorous testsuite of unit, integration, and system tests. Moreover, in a case study with 26developers, all but two found information of automatically detected test holesinteresting in principle. As such, almost half of the 26 would actually act onthe mutant presented to them by adapting an existing or creating a new test.The others did not for a variety of reasons often outside the scope of mutationtesting. It remains a practical challenge how we can include such externalinformation to increase the true actionability rate on mutants.

What It Would Take to Use Mutation Testing in Industry--A Study at Facebook